​Video (image-to-image) registration is a fundamental problem
in computer vision. Registering video frames to the same coordinate system is
necessary before meaningful inference can be made from a dynamic scene in the
presence of camera motion. Standard registration techniques detect specific
structures (e.g. points and lines), find potential correspondences, and use a
random sampling method to choose inlier correspondences. Unlike these
standards, we propose a parameter-free, robust registration method that avoids
explicit structure matching by matching entire images or image patches. We
frame the registration problem in a sparse representation setting, where
outlier pixels are assumed to be sparse in an image. Here, robust video registration
(RVR) becomes equivalent to solving a sequence of `1 minimization problems,
each of which can be solved using the Inexact Augmented Lagrangian Method
(IALM). Our RVR method is made efficient (sublinear complexity in the number of
pixels) by exploiting a hybrid coarse-to-fine and random sampling strategy
along with the temporal smoothness of camera motion. We showcase RVR in the
domain of sports videos, specifically American football. Our experiments on
real-world data show that RVR outperforms standard methods and is useful in
several applications (e.g. automatic panoramic stitching and non-static
background subtraction).

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